Abstract
Digital medicine leverages digital biomarkers by algebraically integrating multiple biomarkers to reflect disease status. Colorimetric analysis offers an intuitive readout, but colorimetric-based digital medicine remains underexplored. Here we show an Enzymatic Colorimetric Encoding-based Digital Medicine platform (EnCODE). By harnessing enzyme-catalyzed multicolor encoding in tandem with the programmability of DNA technology, EnCODE converts multidimensional miRNA information into recognizable optical signals. We demonstrate that these signals are decodable and can be interpreted by visual inspection or spectral analysis, facilitating dimensionality reduction and visualization of disease states. Additionally, EnCODE integrates a continuous weighting mechanism that enables accurate mapping of digital biomarkers. In a cohort of 163 pancreatic cancer clinical samples, EnCODE achieves 96% detection sensitivity and 90% overall accuracy—comparable to the 96% sensitivity and 91% overall accuracy with conventional molecular diagnostic methods. We increase data density through three-dimensional color encoding and hyperspectral imaging-based analysis, enabling an intuitive color-coded molecular readout.
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Data availability
All data supporting the results of this study are available within the paper and its Supplementary Information. The miRNA-seq data used in this study are available from the GEO database under accession codes GSE211692, GSE163031 and GSE106817. Source data are provided with this paper.
Code availability
The R script used in this study to screen potential miRNA targets from the GEO database is provided in the Supplementary Information. The custom code has been uploaded to Zenodo and is available for public access53.
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Acknowledgements
This work was supported by National Key Research and Development Program of China (Grant No. 2023YFC2606100 to X.Z.), National Natural Science Foundation of China (Grant Nos. 22074090 and 32371531 to X.Z., 32301256 to D.M.), Medical Innovation Research Special Project of the Shanghai Science and Technology Innovation Action Plan (Grant No. 23Y11907900 to X.Z.), Natural Science Foundation of Shanghai (Grant No. 23ZR1449100 to D.M.), Shanghai Sailing Program (Grant No. 23YF1432600 to D.M.), Shanghai Municipal Health Commission (Grant Nos. 20244Z0020 to X.Z., 20234Y0005 to D.M.), National Institute of Hospital Administration (Grant No. JYHRJG2024B46 to D.M.), China Industry-University-Research Collaboration Innovation (Grant No. 2025MR009 to D.M.), Shanghai Hospital Development Center Foundation (Grant No. SHDC22023303 to F.S.), and Tongji University Medicine-X Interdisciplinary Research Initiative (Grant No. 2025-0553-ZD-03 to X.Z.).
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C.L. and D.M. conceived the project. C.L., L.W., D.M., J.D., M.Z., and W.C. performed the experiments. W.L., R.Z., J.C., M.Z., and Z.L. provided clinical samples. C.L., X.T. and D.M. analyzed the data and interpreted the results. H.G., Z.M., and X.D. designed the computer programs for data processing. C.L. and D.M. discussed the manuscript. W.L., F.S. and X.Z. supervised the project. C.L. and D.M. wrote the manuscript. All authors joined in the critical discussion and approved the final version.
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Mao, D., Liu, C., Zhang, R. et al. Enzymatic colorimetric encoding-based digital medicine for pancreatic cancer diagnosis. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70343-0
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DOI: https://doi.org/10.1038/s41467-026-70343-0
